{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        activate_func = nn.Sigmoid()\n",
    "        # activate_func = nn.LeakyReLU(0.02)\n",
    "        \n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784, 100),\n",
    "            # activate_func,\n",
    "            nn.LeakyReLU(),\n",
    "            nn.LayerNorm(100),\n",
    "            nn.Linear(100, 10),\n",
    "            # activate_func,\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "        self.loss_function = nn.MSELoss()\n",
    "        # self.loss_function = nn.BCELoss()\n",
    "\n",
    "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.02)\n",
    "        # self.optimiser = torch.optim.Adam(self.parameters())\n",
    "\n",
    "        # record progress\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "\n",
    "    def train(self, inputs, targets):\n",
    "        outputs = self.forward(inputs)\n",
    "        loss = self.loss_function(outputs, targets)\n",
    "\n",
    "        self.optimiser.zero_grad() # set gradient to 0\n",
    "        loss.backward() # backward loss and calculate gradient\n",
    "        self.optimiser.step() # update parameters\n",
    "\n",
    "        # record progress\n",
    "        self.counter += 1\n",
    "        if self.counter % 10 == 0:\n",
    "            self.progress.append(loss.item())\n",
    "        if self.counter % 10000 == 0:\n",
    "            print(f'counter: {self.counter}')\n",
    "\n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(18, 8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistDataset(Dataset):\n",
    "    def __init__(self, csv_file):\n",
    "        self.data_df = pd.read_csv(csv_file, header=None)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_df)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        label = self.data_df.iloc[index, 0]\n",
    "        target = torch.zeros(10)\n",
    "        target[label] = 1.0\n",
    "        img_values = torch.FloatTensor(self.data_df.iloc[index, 1:].values) / 255.0\n",
    "        return label, img_values, target\n",
    "\n",
    "    def plot_image(self, index):\n",
    "        arr = self.data_df.iloc[index, 1:].values.reshape(28, 28)\n",
    "        plt.title(f'label = {self.data_df.iloc[index, 0]}')\n",
    "        plt.imshow(arr, interpolation='none', cmap='Blues')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dataset = MnistDataset('mnist_dataset/mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training epoch: 0\n",
      "counter: 10000\n",
      "counter: 20000\n",
      "counter: 30000\n",
      "counter: 40000\n",
      "counter: 50000\n",
      "counter: 60000\n",
      "training epoch: 1\n",
      "counter: 70000\n",
      "counter: 80000\n",
      "counter: 90000\n",
      "counter: 100000\n",
      "counter: 110000\n",
      "counter: 120000\n",
      "training epoch: 2\n",
      "counter: 130000\n",
      "counter: 140000\n",
      "counter: 150000\n",
      "counter: 160000\n",
      "counter: 170000\n",
      "counter: 180000\n",
      "Wall time: 3min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "epochs = 3\n",
    "C = Classifier()\n",
    "for i in range(epochs):\n",
    "    print(f'training epoch: {i}')\n",
    "    for label, image_data_tensor, target_tensor in mnist_dataset:\n",
    "        C.train(image_data_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_test_data = MnistDataset('mnist_dataset/mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2MxOYEhEjxfTTkqZ0s5kJ1BzGu5P2Gma8Z167RoY/bxYn6N7s5Ig4SdICSRcXh6s9KSrvwXrp2mldw3h3ygTDjP9KN1+7Roc/b1Y3wr5V0vRxvx9TzOsJEbG1+Lld0u3qvaGot+0ZQbf4ub3L/fxKLw3jPdEw4+qB166bw593I+yPSJpl+zjbkyR9TNKqLvTxJrYnFydOZHuypA+q94aiXiVpcTG9WNKdXezlDXplGO9qw4yry69d14c/j4iOPyQtVOWM/M8lfbEbPVTp63hJ/108NnS7N0m3qHJY94oq5zYukPQbklZLekLS/ZKO6qHebpb0mKR1qgRrapd6O1mVQ/R1ktYWj4Xdfu1K+urI68bHZYEkOEEHJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0n8P2CPuWgQoC90AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "record = 43\n",
    "mnist_test_data.plot_image(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data = mnist_test_data[record][1]\n",
    "output = C.forward(image_data)\n",
    "pd.DataFrame(output.detach().numpy()).plot(kind='bar', legend=False, ylim=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9641 10000 0.9641\n"
     ]
    }
   ],
   "source": [
    "score = 0\n",
    "total = 0\n",
    "for label, image_data_tensor, target_tensor in mnist_test_data:\n",
    "    answer = C.forward(image_data_tensor).detach().numpy()\n",
    "    if answer.argmax() == label:\n",
    "        score += 1\n",
    "    total += 1\n",
    "print(score, total, score/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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